Welcome to the exciting world of drone simulation! In this guide, we’ll navigate through installing and utilizing the gym-pybullet-drones library, a minimalist implementation for compatible simulation with various libraries like Gymnasium, Stable Baselines3, and Betaflight. Let’s get started!

Installation Steps

This library has been tested on Intel x64 Ubuntu 22.04 and Apple Silicon macOS 14.1. Below are the detailed steps to get it up and running:

• Clone the repository:

git clone https://github.com/utiasDSL/gym-pybullet-drones.git

• Change directory:

cd gym-pybullet-drones

• Create a conda environment:

conda create -n drones python=3.10

• Activate the environment:

conda activate drones

• Upgrade pip:

pip3 install --upgrade pip

• Install the gym-pybullet-drones library:

pip3 install -e .

Note: If you encounter issues, you may need to install build-essential:

sudo apt install build-essential

Using the Library

Now that we have the library installed, let’s dive into how we can utilize it:

Control Examples

To demonstrate how the drones can be controlled, you can run the following scripts:

• For position and velocity reference:

python3 pid.py

python3 pid_velocity.py

• To see the downwash effect:

python3 downwash.py

Reinforcement Learning Examples

You can also explore reinforcement learning using Stable Baselines3:

• To learn how to hover at a specific height:

python learn.py

• For a multi-agent scenario:

python learn.py --multiagent true

Understanding the Code Through Analogy

Imagine that setting up the gym-pybullet-drones library is akin to assembling a LEGO model, where each step represents a different part of the process. You need to gather all your LEGO pieces (installation files), follow the instruction manual (the steps provided above), and finally piece everything together (using the library). Just as you might encounter difficulties if you miss a step or don’t have all the pieces, the same applies when installing and utilizing the library. Missing dependencies or incorrect setups could lead to errors, but don’t worry; we’ll cover troubleshooting shortly!

Troubleshooting

If you encounter any issues, here are some troubleshooting tips:

• If you are using Ubuntu with an NVIDIA card and see a “Failed to create an OpenGL context” message, try the following steps:
• Launch nvidia-settings and select NVIDIA (Performance Mode) under PRIME Profiles.
• Reboot your system and try again.
• Run all tests from the top folder:

sh pytest tests

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.